Senior Machine Learning Engineer

Why join Freenome?

Freenome is a high-growth biotech company developing tests to detect cancer using a standard blood draw. To do this, Freenome uses a multiomics platform that combines tumor and non-tumor signals with machine learning to find cancer in its earliest, most-treatable stages. 

Cancer is relentless. This is why Freenome is building the clinical, economic, and operational evidence to drive cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, and it’s just the beginning. 

Founded in 2014, Freenome has ~400 employees and continues to grow to match the scope of our ambitions to provide access to better screening and earlier cancer detection.  

At Freenome, we aim to impact patients by empowering everyone to prevent, detect, and treat their disease. This, together with our high-performing culture of respect and cross-collaboration, is what motivates us to make every day count.

Become a Freenomer

Do you have what it takes to be a Freenomer? A “Freenomer” is a determined, mission-driven, results-oriented employee fueled by the opportunity to change the landscape of cancer and make a positive impact on patients’ lives. Freenomers bring their diverse experience, expertise, and personal perspective to solve problems and push to achieve what’s possible, one breakthrough at a time.

About this opportunity:

At Freenome, we are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment. 

The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimizing hardware utilization for efficient training, and performing model optimizations. As part of an interdisciplinary R&D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission of reducing cancer mortality via accessible early detection.

The role reports to the Director of Machine Learning Science. This can be a hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

What you’ll do:

• Implement and refine DL pipelines on distributed computing platforms enhancing the speed and efficiency of DL operations including model training, data handling, model management, and inference.
• Collaborate closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines you create are perfectly aligned with scientific goals and operational needs.
• Continuously monitor, evaluate, and optimize DL model training pipelines for performance and scalability.
• Stay up to date with the latest advancements in AI, ML, and related technologies, and quickly learn and adapt new tools and frameworks, if necessary.
• Develop and maintain robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.
• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation pipelines.
• Act as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.

Must haves:

• MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.

• 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.

• Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.

• Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.

• In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.

• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.

• Understanding of containerization technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.

• Proven track record of developing and optimizing workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.

• Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).

• Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.

• Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.

• Excellent ability to work effectively with cross-functional teams and communicate across disciplines. 

Nice to haves:

• Experience working with large-scale genomics or biological datasets.

• Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.

• Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).

• Experience with infrastructure-as-code and configuration management.

• Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.

• Strong track record of contributions to relevant DL projects, e.g. on github.

Benefits and additional information:

The US target range of our